The nonsystematic occurrence of illness in a community causes the demand for hospital services to be stochastic. To meet this uncertainty hospitals typically set aside enough excess capacity to admit any patient who is in need of a bed. This capacity is, however, expensive. It is therefore crucial to policy makers that the true costs of stochastic demand be accurately measured. This study, using multiple regression analysis and a stochastic model derived from a birth-death queuing model, will identify those factors that cause a hospital to retain a reserve margin. Thus, the hospital's responses to uncertain demand can be empirically revealed and measured. This empirical measure of the hospital's response to stochastic demand will then be introduced into the hospital's cost function to assess the costs of uncertain demand to the hospital. Controlling the spiraling increases in health care costs has been designated as a national priority. Knowledge of the true structure of hospital costs is imperative to policy makers seeking to control these costs. This project will contribute a valuable tool to these policy makers.